Risk monitoring system

ABSTRACT

Various embodiments of the present invention set forth techniques for monitoring risk in a computing system. The technique includes creating one or more risk objects, where each risk object of the one or more risk objects has a corresponding stored risk definition, the stored risk definition associating the risk object with raw machine data pertaining to the risk object, the raw machine data reflecting activity in an information technology (IT) environment. The technique further includes receiving a selection of a first risk object included in the one or more risk objects and receiving a first risk definition that corresponds to the first risk object. The technique further includes performing a search of the raw machine data according to the first risk definition, wherein a risk is identified based on the search of the raw machine data and performing an action based on identifying the risk.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of the co-pending U.S. patentapplication titled, “RISK MONITORING SYSTEM,” filed on Apr. 28, 2017 andhaving Ser. No. 15/582,564. The subject matter of this relatedapplication is hereby incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

Embodiments of the present invention relate generally to computernetworks and, more specifically, to a risk monitoring system.

Description of the Related Art

In the field of computing systems, the amount of computer data generatedby various applications and devices continues to increase, while thecost of storing such data continues to decrease. Accordingly, storinglarge amounts of data inexpensively has become more practical. However,searching and/or operating on stored data, such as information relatedto the operation or security of a computing system, has presentedvarious challenges.

In particular, many large enterprises that generate and store largeamounts of data may allow users to access the data to perform varioustasks, such as by enabling a user to perform searches within data usingsearch query commands and/or machine-learning algorithms. For example, auser may enter search query commands that comply with a particularcomputer language syntax, such as a search processing language (SPL) orother computer programming language.

One drawback to these types of conventional approaches is that searchingand/or analyzing data effectively often requires a high degree ofproficiency in one or more computer languages. In addition, in order toeffectively search for and analyze data, the user must understand thepurpose of the search, such as the relevance of the data to a particularbusiness objective. For example, a user that is analyzing machine datain order to detect fraud in a financial services platform must beproficient in one or more relevant computer languages and must alsounderstand various aspects of the financial services industry in orderto determine which portions of the data are relevant to a fraud inquiry.

However, while some users may possess either proficiency in a computerlanguage or knowledge of a particular industry (e.g., the financialservices industry), few users have sufficient knowledge in both. Forexample, in many large enterprises, an analyst or executive seeking toanalyze a particular set of data may understand which portions of thedata are relevant to a particular task. However, the analyst orexecutive may not understand the relevant computer languages that areneeded to structure a search query to access that data. By contrast, acomputer programmer may possess the requisite technical knowledge tostructure a search query, but the programmer may not understand therelevance of the underlying data to a particular business objective. Asa result, conventional approaches for searching for different types ofdata often require multiple users having different skill sets tocollaborate in an inefficient manner each time data is searched and/oranalyzed.

As the foregoing illustrates, improved techniques for searching and/oranalyzing computer data would be useful.

SUMMARY OF THE INVENTION

Various embodiments of the present invention set forth acomputer-implemented method for monitoring risk in a computing system.The method includes creating one or more risk objects, where each riskobject of the one or more risk objects has a corresponding stored riskdefinition, the stored risk definition associating the risk object withraw machine data pertaining to the risk object, the raw machine datareflecting activity in an information technology (IT) environment. Themethod further includes receiving a selection of a first risk objectincluded in the one or more risk objects and receiving a first riskdefinition that corresponds to the first risk object. The method furtherincludes performing a search of the raw machine data according to thefirst risk definition, wherein a risk is identified based on the searchof the raw machine data and performing an action based on identifyingthe risk.

Other embodiments of the present invention include, without limitation,a computer-readable medium including instructions for performing one ormore aspects of the disclosed techniques, as well as a computing devicefor performing one or more aspects of the disclosed techniques.

At least one advantage of the disclosed techniques is that, by storingthe risk definitions and searching and analyzing the computer data basedon the stored risk definitions, the risk monitoring system enablescomputer data to be searched without requiring a user to re-entercomplicated search query commands and/or machine-learning algorithmseach time a search is performed. A further advantage of the disclosedtechniques is that, by representing the risk objects and logicaloperators graphically, a user without specific proficiency or technicalknowledge related to a computer language syntax is able to perform andmanipulate searches efficiently.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the inventioncan be understood in detail, a more particular description of theinvention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIG. 7 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 8A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 8B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9 illustrates a block diagram of an example cloud-based data intakeand query system in which an embodiment may be implemented;

FIG. 10 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 11 illustrates another networked computer environment in which anembodiment may be implemented;

FIG. 12 is a more detailed illustration of the risk monitoring system1116 of FIG. 11 in accordance with the disclosed embodiments

FIG. 13 illustrates a user interface for displaying risk definitions andrisk objects in accordance with the disclosed embodiments;

FIGS. 14A-14C illustrate a user interface for displaying and selectingrisk objects in accordance with the disclosed embodiments;

FIGS. 15A-15E illustrate an example user interface for selecting riskobjects and applying logical operators to selected risk objects inaccordance with the disclosed embodiments;

FIGS. 16A through 16B illustrate an example user interface for selectingrisk objects and applying logical operators to selected risk objects inaccordance with the disclosed embodiments;

FIG. 17 illustrates an example dashboard screen for displaying status ofmonitoring computer data related to risk objects in accordance with thedisclosed embodiments;

FIG. 18 is a flow diagram of method steps for creating and storing arisk definition, in accordance with the disclosed embodiments;

FIG. 19 is a flow diagram of method steps for generating a result basedon one or more risk objects and one or more logical operators that arespecified via a UI, in accordance with the disclosed embodiments; and

FIGS. 20A and 20B are flow diagrams of method steps for applying varioustypes of logic to generate a result in a risk monitoring system, inaccordance with the disclosed embodiments.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a more thorough understanding of the present invention. However,it will be apparent to one of skill in the art that embodiments of thepresent invention may be practiced without one or more of these specificdetails.

System Overview

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7 Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Models        -   2.10. Acceleration Techniques            -   2.10.1. Aggregation Technique            -   2.10.2. Keyword Index            -   2.10.3. High Performance Analytics Store            -   2.10.4. Accelerating Report Generation        -   2.11. Security Features        -   2.12. Cloud-Based System Overview        -   2.13. Searching Externally Archived Data            -   2.13.1. ERP Process Features        -   2.14. IT Service Monitoring    -   3.0. Graphical Risk Monitoring System

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events.” An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata.” In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art will understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art will understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks” or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH Affinity”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 7 illustrates how a search query 702received from a client at a search head 210 can split into two phases,including: (1) subtasks 704 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 706 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 702, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 702 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 704, and then distributes searchquery 704 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 706 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “Distributed HighPerformance Analytics Store”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “Compressed Journaling InEvent Tracking Files For Metadata Recovery And Replication”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “Real Time Searching AndReporting”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (STEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 8A illustrates anexample key indicators view 800 that comprises a dashboard, which candisplay a value 801, for various security-related metrics, such asmalware infections 802. It can also display a change in a metric value803, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 800 additionallydisplays a histogram panel 804 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “Key Indicators View”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 8B illustrates an example incident review dashboard 810 thatincludes a set of incident attribute fields 811 that, for example,enables a user to specify a time range field 812 for the displayedevents. It also includes a timeline 813 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 814 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 811. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 9 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2, the networkedcomputer system 900 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system900, one or more forwarders 204 and client devices 902 are coupled to acloud-based data intake and query system 906 via one or more networks904. Network 904 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 902 and forwarders204 to access the system 906. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 906 forfurther processing.

In an embodiment, a cloud-based data intake and query system 906 maycomprise a plurality of system instances 908. In general, each systeminstance 908 may include one or more computing resources managed by aprovider of the cloud-based system 906 made available to a particularsubscriber. The computing resources comprising a system instance 908may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 902 to access a web portal or otherinterface that enables the subscriber to configure an instance 908.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 908) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.13. Searching Externally Archived Data

FIG. 10 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1004 over network connections1020. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 10 illustratesthat multiple client devices 1004 a, 1004 b, . . . , 1004 n maycommunicate with the data intake and query system 108. The clientdevices 1004 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 10 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1004 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1010. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1010, 1012. FIG. 10 shows two ERP processes 1010, 1012 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1014 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1016. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1010, 1012 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1010, 1012 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1010, 1012 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1010, 1012 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1010, 1012 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1014, 1016, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1004 may communicate with the data intake and querysystem 108 through a network interface 1020, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “ExternalResult Provided Process For RetriEving Data Stored Using A DifferentConfiguration Or Protocol”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.13.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the] streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational.’ Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. Graphical Risk Monitoring System

As further described herein, the data intake and query system 108described in conjunction with FIGS. 1-10 can be used in conjunction witha risk monitoring system, described in conjunction with FIGS. 11-20B, inorder to search and analyze computer data based on certain criteriadetermined to be relevant to a particular risk or condition associatedwith the computer system.

In this manner, the risk monitoring system may store these criteria asrisk definitions, and the risk monitoring system may search the computerdata based on these stored risk definitions, thus enabling searching thecomputer data without requiring a user to repeatedly enter complicatedsearch query commands or algorithms each time a search of the computerdata is performed. Further, the risk monitoring system causesrepresentations of these risk definitions, corresponding risk objectswhich represent searches based on the risk definitions, and logicaloperators to be displayed via a user interface (UI). Further, these riskdefinitions, risk objects, and logical operators may be selected and/ormanipulated graphically by a user via the UI to cause the riskmonitoring system to perform a search of the actual computer data basedon a selected stored risk definition, or to operate on and/or combineinformation associated with the selected searches as represented by therisk objects. In this manner, a user is not required to manually orrepeatedly enter combinations of the complicated search query commandsor algorithms, and manually combine these commands or algorithms usinglogical operations, in order to perform a search of the computer data oroperate on information associated with the searches. Rather, the riskmonitoring system enables a user to perform these tasks more efficientlyvia the UI, without requiring a technical proficiency with a particularcomputer programming language syntax that is required by conventionalapproaches.

The risk monitoring system is now described in further detail herein. Invarious embodiments, the computer data being searched may be associatedwith client devices 102, host devices 106, the data intake and querysystem 108, and/or any other devices and systems communicating over oneor more networks 104. The risk monitoring system may receive and analyzedata that is part of an event referred to herein as “event data” andstored within the data intake and query system 108. In addition, therisk monitoring system may receive and analyze raw machine data, such asdata received or retrieved from one or more system log files, streams ofnetwork packet data, sensor data, application program data, error logs,stack traces, system performance data, and so on. In addition, the riskmonitoring system may receive and analyze any other technically feasibleform of computer data.

In the context of “event data,” the data intake and query system 108and/or the risk monitoring system may process raw data to producetimestamped events. The data intake and query system 108 and/or the riskmonitoring system may further store the timestamped events in a datastore. In various embodiments, such a data store may be located ineither or both of the data intake and query system 108 and/or the riskmonitoring system. Although many of the techniques described herein arediscussed with reference to a data intake and query system 108, thesetechniques are also applicable to all other types of data systems.

FIG. 11 illustrates a networked computer environment 1100 in accordancewith the disclosed embodiments. As shown, the networked computerenvironment 1100 includes, without limitation, client devices 102, hostdevices 106, a data intake and query system 108, and a risk monitoringsystem 1116 that communicate with each other over one or more networks104. The networked computer environment 1100 also includes exemplarynetwork connections 1120, 1122, 1124, and 1126 connecting client devices102, host devices 106, the data intake and query system 108, and therisk monitoring system 1116, respectively, via the networks 104.

As shown, an exemplary network connection establishes a connectionbetween a first computing device and a second computing device via oneor more networks 104 and may include network links, switches,communication ports, or any means of connecting the first computingdevice with the second computing device via the one or more networks104. The client devices 102, host devices 106, data intake and querysystem 108, and networks 104 may function substantially the same ascorresponding elements of the networked computer environment 100 of FIG.1 except as described herein.

As described herein, the risk monitoring system 1116 may receive andanalyze any form of computer data, including “event data,” raw machinedata, network traffic data, network traffic packet data, and any otherform of computer data that reflects activity in an informationtechnology (IT) environment. In some embodiments, the risk monitoringsystem 1116 may cause any of client devices 102, host devices 106,and/or the data intake and query system 108 to receive and analyze thecomputer data.

The risk monitoring system 1116 is associated with the one or morecomputer networks 104. Network traffic may be exchanged via the one ormore computer networks 104 in accordance with one or more networkcommunications protocols. In some embodiments, network traffic isexchanged via the one or more computer networks 104 by sending andreceiving data in the form of packets of data, where a transmission unitfor the network traffic is a packet of data. The network traffic datamay be in any format, including, without limitation, raw machine data,event data derived from the raw machine data as further describedherein, metadata regarding data packets exchanged via the connection,data packets, portions of data packets, such as packet headers, anyforms of metadata regarding the network traffic, or any other form ofcomputer data from a data source. The network traffic data may beaccessed from log files associated with client devices 102, log filesassociated with host devices 106, packet capture data derived frommessage traffic over networks 104, or any other type of real-time orarchived data source for data representing network traffic data.

The risk monitoring system 1116 identifies computer data that representspotential risks or conditions related to the computer network or one ormore computers included in the computer network, based on certaindefined criteria related to the particular risks or conditions. In someembodiments, the specific criteria used to determine whether thecomputer data is relevant to a particular risk or condition may bedefined or selected by a user, may be predetermined, and/or may bedetermined in any other technically feasible manner.

The criteria for determining whether computer data is relevant to aparticular risk or condition may be applicable to any field or type ofcomputer data. For instance, the risk monitoring system 1116 may searchfor and analyze computer data related to fraud, where the criteriadetermine which computer data is relevant to incidents or threats offraudulent behavior or transactions by one or more users or computersinteracting with the computer system. Further, the risk monitoringsystem 1116 may search for and analyze computer data related tosecurity, where the criteria determine which computer data is relevantto breaches or threats to the security associated with one or more usersor computers interacting with the computer system. In addition, the riskmonitoring system 1116 may search for and analyze computer data relatedto performance, where the criteria determine which computer data isrelevant to the performance or performance issues within the computersystem. Moreover, the risk monitoring system 1116 may search for andanalyze computer data related to business analytics, where the criteriadetermine which computer data is relevant to certain characteristics ofbusiness transactions within the computer system. In general, the riskmonitoring system 1116 may search for and analyze computer data relatedto any technically feasible type of computer data.

In some embodiments, the risk monitoring system 1116 searches andanalyzes computer data based on one or more search query commands, wherethe search query commands specify the criteria for determining whetherthe computer data is relevant to a particular risk or condition. Thesesearch query commands may be programmed by a user or may bepredetermined. Further, these search query commands may include commandsin a computer programming language or search processing language (SPL),such as SPLUNK® SPL, where the commands comply with a computer languagesyntax, or may include any technically feasible computer commands thatcomply with a computer language syntax.

In some embodiments, the risk monitoring system 1116 searches andanalyzes computer data based on one or more algorithms, where thealgorithms specify the criteria for determining whether the computerdata is relevant to a particular risk or condition. These algorithms,referred to herein as “machine-learning algorithms,” may be programmedby a user or may be predetermined, and may utilize any form of computerprogramming algorithms or any form of machine learning, including,without limitation, neural networks, decision trees, or any other formof machine learning. Further, these machine-learning algorithms mayutilize computer commands and/or algorithms programmed in a computerprogramming language or search processing language (SPL), such asSPLUNK® SPL, where the algorithms may comply with a computer languagesyntax, or may include any technically feasible computer algorithms.

In various embodiments, the risk monitoring system 1116 may store thesecriteria in a memory as a “risk definition.” In this manner, riskdefinitions may be stored for future use in order to enable a user toperform future searches of computer data based on accessing the storedrisk definitions. In this manner, the risk monitoring system 1116 mayperform future searches of computer data based on accessing the storedrisk definitions, without requiring the user to repeatedly entercomplicated search query commands or machine-learning algorithms eachtime a search is performed.

The risk monitoring system 1116 may search the computer data based on arisk definition in response to a user action (e.g., a user request), ina predetermined or automated manner (e.g., on a periodic basis or at aspecified time), or in any other manner. In some embodiments, the riskdefinitions may be stored in the risk monitoring system 1116, in thedata intake and query system 108, in any of the host devices 106, or inany of the client devices 102 in any manner. In some embodiments, therisk definitions may include the one or more search query commandsthemselves, information specifying the applicable search query commands,the machine-learning algorithms themselves, information specifying theapplicable machine-learning algorithms, or any form of informationidentifying the criteria for determining whether computer data isrelevant to a particular risk or condition.

In various embodiments, the risk monitoring system 1116 may causerepresentations of one or more risk definitions to be displayed to auser through a UI, such as a graphical user interface (GUI). In someembodiments, the risk monitoring system 1116 may receive a selection viathe UI, such as a user selection or an automated selection, to create anew risk definition or modify one or more of the stored riskdefinitions. For instance, a user may enter search query commands and/ormachine-learning algorithms via the UI to create or modify a stored riskdefinition.

In various embodiments, the risk monitoring system 1116 may causerepresentations of a “risk object,” which corresponds to a stored riskdefinition, to be displayed to a user via the UI. The risk object isrepresented to the user to identify the search of the computer databased on the stored risk definition and/or to identify a characteristicassociated with the computer data resulting from the search, such as arisk score resulting from the search, as further described herein. Insome embodiments, the risk monitoring system 1116 may receive aselection to form a group of two or more risk objects via the UI using aselection mechanism, such as by graphical selection via the UI (e.g., bydragging-and-dropping the selected risk objects onto a canvas located inthe UI, or using highlighting or selection box mechanisms), any form ofkeyboard interaction, and so forth.

Further, the risk monitoring system 1116 may receive a selection via theUI, such as a user selection or an automated selection, to operate on orcombine one or more of the displayed risk objects and/or groups of riskobjects, in order to generate some form of result. For instance, a usermay select two or more of the displayed risk objects to generate acombined result by combining or operating on information, such as riskscores, associated with the selected risk objects. In this manner, auser may define a “threat” as occurring when a particular combination ofrisk objects and/or groups of risk objects produces a particular result.In some embodiments, the user may select the one or more risk objectsand/or groups of risk objects via a selection mechanism, such as bygraphical selection via the UI, as described herein.

The risk monitoring system 1116 may cause a representation of one ormore logical operators for operating on or combining the selected riskobjects and/or groups of risk objects to be displayed via the UI, forselection by a user. In some embodiments, the logical operators mayinclude any form of operators for operating on or combining informationrepresenting the selected risk objects in any technically-feasiblemanner. In various embodiments, the representations of the logicaloperators displayed in the UI may include one or more fields for a userto input or select the logical operators to be used. For instance, inthe selection of the one or more logical operators, a user may inputinformation for the operators, such as via a text input field, or a usermay select operators, such as a via a graphical pull-down or drop-downmenu, and so forth.

Once the risk monitoring system 1116 receives a selection of the one ormore risk objects and/or groups of risk objects and the one or morelogical operators, the risk monitoring system 1116 causes a result to begenerated based on operating on and/or combining the selected one ormore risk objects and/or groups of risk objects using the selected oneor more logical operators. The generated result may include some form ofmetric or indicator identifying a characteristic of the computer datafound to be relevant to the selected one or more risk objects and/orgroups of risk objects.

In various embodiments, the risk monitoring system 1116 determineswhether a “threat” is detected based on the generated result ofoperating on and/or combining the selected one or more risk objectsand/or groups of risk objects. Subsequently, the risk monitoring system1116 may perform an “action” when a threat is detected. In someembodiments, an action performed by the risk monitoring system 1116 mayvary depending on the relevant field or type of computer data associatedwith the search. For instance, when searching for and analyzing computerdata related to fraud, security, performance issues, or businessanalytics, an action performed by the risk monitoring system 1116 mayinclude, without limitation, issuing a warning regarding detectedthreats, sending an email to a particular user's address when a threatis detected, generating a ticket to be processed to remedy the threat,running a computer script, mitigating the threat, or suppressing thethreat when the threat is determined to be a false alarm.

In this manner, the risk monitoring system 1116 enables a user to selectand/or manipulate graphical representations of the risk objects andlogical operators via the UI. Accordingly, a user is not required tomanually or repeatedly enter combinations of the complicated searchquery commands or algorithms, and manually combine these commands oralgorithms using logical operations, in order to perform a search of thecomputer data or operate on or combine information associated with theselected risk objects and/or groups of risk objects. Rather, the riskmonitoring system 1116 enables a user to perform these tasks moreefficiently via the UI, without requiring a technical proficiency with aparticular computer programming language syntax that is required byconventional approaches.

The risk monitoring system 1116 in the networked computer environment1100 is represented as being implemented via a separate system in FIG.11. Those skilled in the art will understand that FIG. 11 represents oneexample of a networked computer system, and other embodiments may usedifferent arrangements, including arrangements in which the riskmonitoring system 1116 is implemented, completely or in part, within anyone of or any combination of client devices 102, host devices 106, andthe data intake and query system 108. For example, as shown in FIG. 11,a monitoring component 112 could be included as a client application 110within one of client devices 102, and this monitoring component 112could be executed in conjunction with the risk monitoring system 1116,such that the client devices 102 could be implemented as part of therisk monitoring system 1116. Those skilled in the art will understandthat such a monitoring component 112 may be implemented in any computingdevice or system, such as one or more client devices 102, one or morehost devices 106, and/or the data intake and query system 108.

FIG. 12 is a more detailed illustration of the risk monitoring system1116 of FIG. 11 in accordance with the disclosed embodiments. As shown,the risk monitoring system 1116 includes, without limitation, aprocessor 1202, storage 1204, an input/output (I/O) device interface1206, a network interface 1208, an interconnect 1210, and a systemmemory 1212. The computer system 100 of FIG. 1 can be configured toimplement the risk monitoring system 1116. The processor 1202, storage1204, I/O device interface 1206, network interface 1208, interconnect1210, and system memory 1212 function substantially the same asdescribed in conjunction with FIG. 1 except as further described below.

In general, processor 1202 retrieves and executes programminginstructions stored in the system memory 1212. Processor 1202 may be anytechnically feasible form of processing device configured to processdata and execute program code. Processor 1202 could be, for example, acentral processing unit (CPU), a graphics processing unit (GPU), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and so forth. Processor 1202 stores and retrievesapplication data residing in the system memory 1212. Processor 1202 isincluded to be representative of a single CPU, multiple CPUs, a singleCPU having multiple processing cores, and the like. In operation,processor 1202 is the master processor of risk monitoring system 1116,controlling and coordinating operations of other system components.System memory 1212 stores software applications and data for use byprocessor 1202. Processor 1202 executes software applications storedwithin system memory 1212 and optionally an operating system. Inparticular, processor 1202 executes software and then performs one ormore of the functions and operations set forth in the presentapplication.

The storage 1204 may be a disk drive storage device. Although shown as asingle unit, the storage 1204 may be a combination of fixed and/orremovable storage devices, such as fixed disc drives, floppy discdrives, tape drives, removable memory cards, or optical storage, networkattached storage (NAS), or a storage area-network (SAN). Processor 1202communicates to other computing devices and systems via networkinterface 1208, where network interface 1208 is configured to transmitand receive data via a communications network.

The interconnect 1210 facilitates transmission, such as of programminginstructions and application data, between the processor 1202,input/output (I/O) devices interface 1206, storage 1204, networkinterface 1208, and system memory 1212. The I/O devices interface 1206is configured to receive input data from user I/O devices 1222. Examplesof user I/O devices 1222 may include one or more buttons, a keyboard,and a mouse or other pointing device. The I/O devices interface 1206 mayalso include an audio output unit configured to generate an electricalaudio output signal, and user I/O devices 1222 may further include aspeaker configured to generate an acoustic output in response to theelectrical audio output signal. Another example of a user I/O device1222 is a display device that generally represents any technicallyfeasible means for generating an image for display. For example, thedisplay device may be a liquid crystal display (LCD) display, CRTdisplay, or DLP display. The display device may be a TV that includes abroadcast or cable tuner for receiving digital or analog televisionsignals.

The system memory 1212 includes, without limitation, a risk monitoringprogram 1230 and a data store 1240. The risk monitoring program 1230includes, without limitation, a search query command analyzer 1231 and amachine-learning algorithm search analyzer 1232. The data store 1240includes without limitation, storage for storing risk definitions, suchas a risk definition 1 1241, a risk definition 2 1242, and a riskdefinition 3 1243. As explained herein, the risk definitions may bestored in the risk monitoring system 1116, in the data intake and querysystem 108, in any of the host devices 106, or in any of the clientdevices 102 in any manner.

The risk monitoring program 1230 may receive and analyze data that ispart of an event referred to herein as “event data” and stored withinthe data intake and query system 108. The risk monitoring program 1230may receive and analyze machine data (e.g., raw machine data) receivedvia the interconnect 1210, and the interconnect 1210 may receive themachine data from one or more networks 104 via the network interface1208. In addition, the risk monitoring program 1230 may receive andanalyze machine data, such as machine data received or retrieved fromone or more system log files, streams of network packet data, sensordata, application program data, error logs, stack traces, systemperformance data, and so on. The machine data may be in any format,including, without limitation, raw machine data or SPLUNK® events. Insome embodiments, the risk monitoring program 1230 may perform anextract, transform, and load (ETL) process on the incoming machine datato generate data in a format that is amenable to further analysis. In sodoing, the risk monitoring program 1230 may remove or otherwise filterdata that is not relevant to the applicable risk definitions whileretaining relevant data. The risk monitoring program 1230, including thesearch query command analyzer 1231 and the machine-learning algorithmsearch analyzer 1232, is now described in further detail.

The search query command analyzer 1231 and/or the machine-learningalgorithm search analyzer 1232 may receive raw machine data via theinterconnect 1210 of the risk monitoring system 1116. The search querycommand analyzer 1231 and/or the machine-learning algorithm searchanalyzer 1232 may receive raw machine data from one or more clientdevices 102, one or more host devices 106, and/or the data intake andquery system 108 via one or more networks 104. The raw machine data maybe accessible from log files associated with client devices 102 and/orhost devices 106, data generated by the data intake and query system108, packet capture data derived from message traffic over networks 104,or any other technical feasible raw machine data from a data source.

The search query command analyzer 1231 searches and analyzes the machinedata based on search query commands, in order to search for machine datathat is relevant to criteria specified by the search query commands. Asdescribed herein, the one or more search query commands may beconfigured to search for machine data based on certain criteria that isdefined in one or more stored risk definitions. In some embodiments, thesearch query commands may themselves be included in or specified by thecorresponding risk definitions.

The machine-learning algorithm search analyzer 1232 searches andanalyzes the machine data based on one or more machine-learningalgorithms, in order to search for machine data that is relevant tocriteria specified by the machine-learning algorithms. As describedherein, the one or more machine-learning algorithms may be configured tosearch for machine data based on certain criteria that is defined in oneor more stored risk definitions. In some embodiments, themachine-learning algorithms may themselves be included in or specifiedby the corresponding risk definitions.

Those skilled in the art will understand that parts or all of the riskmonitoring program 1230 and/or the search query command analyzer 1231may be executed or implemented in some manner to search and analyzemachine data according to search query commands. Thus, when referring tosearching and analyzing machine data with respect to search querycommands, those skilled in the art will understand that referring to therisk monitoring program 1230 refers to parts or all of the riskmonitoring program 1230 and/or parts or all of the search query commandanalyzer 1231.

Further, those skilled in the art will understand that parts or all ofthe risk monitoring program 1230 and/or the machine-learning algorithmsearch analyzer 1232 may be executed or implemented in some manner tosearch and analyze machine data according to algorithms as describedherein. Thus, when referring to searching and analyzing machine datawith respect to machine-learning algorithms, those skilled in the artwill understand that referring to the risk monitoring program 1230refers to parts or all of the risk monitoring program 1230 and/or partsor all of the machine-learning algorithm search analyzer 1232.

In various embodiments, parts or all of the risk monitoring program1230, in conjunction with the search query command analyzer 1231 and/orthe machine-learning algorithm search analyzer 1232, may perform theprocesses performed by the risk monitoring system 1116 relating tosearching and analyzing the machine data, and so forth, as describedherein.

In various embodiments, the risk monitoring program 1230 may search andanalyze the machine data to determine which portions of the machine dataare relevant to certain risks or conditions applicable to any field ortype of machine data, such as fraud, security, performance, businessanalytics, or any other field or type of machine data.

Further, in various embodiments, the risk monitoring program 1230 maycreate one or more risk definitions to identify criteria for determiningwhich portions of machine data are relevant to a particular risk orcondition, and to store these risk definitions in a memory for futureuse in order to enable a user to perform future searches of the machinedata. As described herein, the risk definitions may be stored in thedata store 1240 in the risk monitoring system 1116, in the data intakeand query system 108, in any of the host devices 106, in any of theclient devices 102, or in any form of memory in any technically feasiblemanner.

In various embodiments, the risk monitoring program 1230 may access astored risk definition and perform a search of the machine data based onone or more of the stored risk definitions. In this manner, the riskmonitoring program 1230 may perform future searches of machine databased on accessing the stored risk definitions, without requiring theuser to repeatedly enter complicated search query commands ormachine-learning algorithms each time a search is performed.

Further, the risk monitoring program 1230 may represent risk objectsthat correspond to the stored risk definitions in a UI, to identify thesearch of the machine data based on the stored risk definition and/or toidentify a characteristic associated with the machine data resultingfrom the search, such as a risk score resulting from the search, asdescribed herein. In some embodiments, the risk monitoring program 1230may receive a selection of one or more of risk objects to form a groupof risk objects, and the risk monitoring program 1230 may receive aselection of one or more risk objects and/or groups of risk objects thatare to be operated on or combined in some manner.

In some embodiments, the risk monitoring program 1230 may cause arepresentation of one or more logical operators for operating on and/orcombining the one or more selected risk objects and/or groups of riskobjects to be displayed via the UI. In addition, the risk monitoringprogram 1230 may receive a selection of one or more of the displayedlogical operators and may receive additional information from a userindicating how the logical operators are to be applied. Once the riskmonitoring program 1230 receives a selection of the one or more riskobjects and/or groups of risk objects, and the one or more logicaloperators, the risk monitoring program 1230 may cause a result to begenerated based on operating on and/or combining the selected one ormore risk objects and/or groups of risk objects using the selected oneor more logical operators. In this manner, the risk monitoring program1230 enables a user to select and/or manipulate graphicalrepresentations of the risk objects and logical operators via the UI.Accordingly, a user is not required to manually or repeatedly entercombinations of the complicated search query commands or algorithms, andmanually combine these commands or algorithms using logical operations,in order to perform a search of the machine data or operate on orcombine information associated with the selected risk objects and/orgroups of risk objects. Rather, the risk monitoring program 1230 enablesa user to perform these tasks more efficiently via the UI, withoutrequiring a technical proficiency with a particular computer programminglanguage syntax that is required by conventional approaches.

In some embodiments, the data store 1240 may also store the machine datain any format, including, without limitation, as raw machine data, asevent data derived from the raw machine data, etc. In some embodiments,the machine data may be received from the data intake and query system108, one or more client devices 102, and/or one or more host devices106. The streaming data may include log files associated with clientdevices, packet capture data derived from message traffic over acomputer network, or any other technical feasible data source.

FIG. 13 illustrates a user interface for displaying risk definitions andrisk objects in accordance with the disclosed embodiments. As shown,FIG. 13 shows a user interface 1300 being displayed that includes a riskobject portion 1310 for displaying a listing of one or more risk objectsor groups of risk objects, where each risk object is associated with astored risk definition. Those skilled in the art will understand thatthe risk object portion 1310 may include any number of risk objects orgroups of risk objects related to any field or type of machine data(e.g., raw machine data), including, without limitation, fraud,security, performance, business analytics, and so forth.

As shown, the user interface 1300 also includes a risk object creationand modification portion 1320 that displays a risk definition thatcorresponds to one of the risk objects listed in risk object portion1310. In some embodiments, a user may create or modify a risk definitionthat corresponds to one of the risk objects listed in the risk objectportion 1310. Alternatively, in some embodiments, the risk definitionsmay be predetermined or determined in an automated fashion. Thoseskilled in the art will understand that the risk object creation andmodification portion 1320 may include risk definitions associated withany number of risk objects related to any field or type of machine data,including, without limitation, fraud, security, performance, businessanalytics, and so forth.

Further, as shown, the risk object creation and modification portion1320 includes a search criteria portion 1330, which shows an exemplarysearch query command to be used as part of the risk definition, toidentify which machine data is relevant to a particular risk orcondition. Those skilled in the art will understand that the searchcriteria portion 1330 is shown as including a search query command byway of example only, and the search criteria portion 1330 may includeany type of representation of criteria for determining which machinedata is relevant for a particular search, including, without limitation,search query commands, machine-learning algorithms, and so forth. Asshown, the risk object creation and modification portion 1320 alsoincludes a preview section 1340, which shows a preview of machine datathat is relevant to or meets the search query command shown in thesearch criteria portion 1330.

As shown, the risk object creation and modification portion 1320 alsoincludes a risk score portion 1350, which displays a prospective riskscore associated with the risk object as generated in accordance withthe risk definition. Further, the risk definition may include a formulafor determining the risk score of the corresponding risk object based onthe machine data found to be meet the criteria specified in the riskdefinition. As shown, the risk score portion 1350 displays a prospectiverisk score via a number and a color code, as indicated by shading. Forinstance a high risk score may indicate that a particular risk orcondition is more serious, more urgent, and/or more likely.Alternatively, the numbering of the risk score may be reversed, where alower risk score indicates that a particular risk or condition is moreserious, more urgent, and/or more likely. Those skilled in the art willunderstand that the risk score portion 1350 is shown as including aprospective risk score by way of example only, and the risk scoreportion 1350 may include any type of risk scores or othercharacteristics associated with the risk object, including, withoutlimitation, a risk score, a risk severity level, a risk probabilitylevel, a time order of the risk or condition, and so forth.

As shown, the risk object creation and modification portion 1320 alsoincludes a “save” button 1360 to indicate that the risk definition maybe saved in a memory for future use in order to enable a user to performfuture searches of the machine data. As described herein, the riskdefinitions may be stored in the data store 1240 in the risk monitoringsystem 1116, in the data intake and query system 108, in any of the hostdevices 106, in any of the client devices 102, or in any form of memoryin any technically feasible manner. As described herein, the riskmonitoring system 1116 may enable a user to perform future searches ofmachine data based on accessing the stored risk definitions, withoutrequiring the user to repeatedly enter complicated search query commandsor machine-learning algorithms each time a search is performed.

In an exemplary risk definition, a user of the risk monitoring system1116 may seek all machine data related to transactions involving a userX over a threshold amount of money Y, where transactions involving overthe threshold amount of money may indicate suspicious or fraudulentactivity. For such a scenario, a user of the risk monitoring system 1116may create a risk definition to include a search criteria portion 1330that specifies the criteria that the user must equal X, and the amountof money transacted must be greater than Y. Such a risk definition mayinclude any commands or algorithms used to establish these criteria.Further, this risk definition may be stored in a memory, as describedherein. Subsequently, the risk monitoring system 1116 may perform anactual search of the machine data to determine which machine data meetsthe criteria as defined in the stored risk definition.

In another exemplary risk definition, a user of the risk monitoringsystem 1116 may seek all machine data related to a change of password onan account, followed by a large financial transfer out of that account,since such transactions occurring in that specific order may representfraudulent activity. For such a scenario, a user of the risk monitoringsystem 1116 may create and store a risk definition to include a searchcriteria portion 1330 that specifies the criteria that the transactionmust change a password, followed by a transfer of money. Again,subsequently, the risk monitoring system 1116 may perform an actualsearch of the machine data to determine which machine data meets thecriteria as defined in the stored risk definition. For such a scenario,a risk object may be displayed as including a time order indicatorindicating the time order of the particular activities of the risk orcondition.

FIGS. 14A through 14C illustrate a user interface for displaying andselecting risk objects in accordance with the disclosed embodiments. Asshown, FIGS. 14A through 14C show a user interface 1400 being displayedthat includes a risk object portion 1410 for displaying a listing of oneor more risk objects or groups of risk objects, where each risk objectis associated with a stored risk definition. Those skilled in the artwill understand that the risk object portion 1410 may include any numberof risk objects or groups of risk objects related to any field or typeof machine data, including, without limitation, fraud, security,performance, business analytics, and so forth.

As shown, the user interface 1400 also includes a filter portion 1405,for determining which risk objects and/or groups of risk objects are tobe listed in the risk object portion 1410. As shown, the filter portion1405 may determine that only risk objects or groups of risk objectshaving a particular severity level, such as “critical,” may be listed inrisk object portion 1410. In some embodiments, a severity level of arisk object may be predetermined or determined in an automated mannerbased only on the risk definition. For instance, for risk definitionswhere the criteria specify that the search seeks machine data related tocertain fraudulent transactions, the corresponding risk objects may beassigned a particular high severity level, such as “critical.” In someembodiments, a severity level of a risk object may be determined basedon the actual machine data found to be relevant to the particular riskor condition. For instance, when the relevant machine data indicates aparticular transaction involves over a threshold amount of money, theseverity level of the risk object may be actively assigned to be a highseverity level, such as “critical,” whereas when the machine datarepresents no money movement or a transfer of a minimal amount of moneymay be assigned a lower severity level.

Further, as shown, the filter portion 1405 may determine that only riskobjects or groups of risk objects having a certain status may be listedin risk object portion 1410, such as when the risk objects have a statusof enabled, disabled, or draft.

As shown, the user interface 1400 also includes a canvas portion 1420for displaying one or more selected risk objects. Specifically, asshown, the canvas portion 1420 enables a selection of one or more riskobjects, and the selected risk objects may be “dragged” from the riskobject portion 1410 onto the canvas to be displayed on the canvasportion 1420, as further described herein. Alternatively, as shown, thecanvas portion 1420 enables a user to create a new risk object, whichincludes creating a new risk definition. Those skilled in the art willunderstand that the canvas portion 1420 may enable creating any numberof risk objects related to any field or type of machine data, including,without limitation, fraud, security, performance, business analytics,and so forth.

In addition, as shown in FIG. 14B, the user interface 1400 enables aselection of one or more risk objects from the risk object portion 1410.For instance, as shown, a user may select two risk objects 1430 from therisk object portion 1410, and the user may drag and drop these two riskobjects 1430 onto the canvas portion 1420, as shown by the dragging anddropping indicator 1440. Those skilled in the art will understand thatthe risk objects 1430 are shown as being selected by a user by way ofexample only, and the risk objects 1430 may be selected in any manner,including, without limitation, by a user or in an automated fashion.

In addition, as shown in FIG. 14C, dragging and dropping the two riskobjects 1430 onto the canvas portion 1420 causes the user interface 1400to display these two risk objects on portion 1450 of the canvas portion1420. Further, as shown, portion 1450 of the canvas portion 1420displays a first risk object 1451 as represented by a title 1451A thatidentifies that the risk object is associated with an anomalous IPaddress for a user, along with a first risk score 1451B showing a riskscore equal to 38. In addition, as shown, portion 1450 of the canvasportion 1420 also displays a second generated risk object 1452 asrepresented by a title 1452A that identifies that the risk object isassociated with an anomalous browser used by a user, along with a firstrisk score 1452B showing a risk score equal to 35. Those skilled in theart will understand that the titles 1451A and 1452A are displayed asparticular titles, and risk scores 1451B and 1452B are displayed asparticular risk scores by way of example only, and titles 1451A and1452A may include any kind of descriptions of the risk objects, and therisk scores 1451B and 1452B may include any type of risk scores or othercharacteristics associated with the risk objects, including, withoutlimitation, a risk score, a risk severity level, a risk probabilitylevel, an order or precedence indicator, and so forth.

FIGS. 15A through 15E illustrate an example user interface for selectingrisk objects and applying logical operators to selected risk objects inaccordance with the disclosed embodiments. As shown, FIGS. 15A through15E show a user interface 1500 being displayed that includes a riskobject portion 1510 for displaying a listing of one or more risk objectsand/or groups of risk objects, where each risk object is associated witha stored risk definition. Those skilled in the art will understand thatthe risk object portion 1510 may include any number of risk objectsand/or groups of risk objects related to any field or type of machinedata, including, without limitation, fraud, security, performance,business analytics, and so forth.

As shown, the user interface 1500 also includes a canvas portion 1520for displaying one or more selected risk objects. Specifically, as shownin FIG. 15A, two risk objects 1551 and 1552 are displayed on portion1550 of the canvas portion 1520. Those skilled in the art willunderstand that the canvas portion 1520 is shown as displaying two riskobjects by way of example only, and that the canvas portion 1520 maydisplay any number of risk objects and/or groups of risk objects.

As shown in FIG. 15A, the two risk objects 1551 and 1552 displayed onportion 1550 of the canvas may be selected for further processing via auser selection. As shown, a user may utilize a user selection box 1560to begin to select the two risk objects 1551 and 1552 for furtherprocessing. Those skilled in the art will understand that the userselection box 1560 being utilized by a user is shown by way of exampleonly, and that any number of risk objects and/or groups of risk objectsmay be selected in any manner, including, without limitation, via useraction or in an automated fashion.

As shown in FIGS. 15B through 15E, a user may utilize the user selectionbox 1560 to complete a selection of the two risk objects 1551 and 1552displayed in the canvas portion 1520 for further processing as a groupof selected risk objects 1561. As also shown in FIGS. 15B through 15E,information about each of the risk objects included in the group ofselected risk objects 1561, may be displayed within the user selectionbox 1560. As shown, within the user selection box 1560, a first riskobject 1551 is represented by a title 1551A that identifies that therisk object is associated with an anomalous IP address for a user, alongwith a first risk score 1551B showing a risk score equal to 38. Inaddition, as shown, within the user selection box 1560, a second riskobject 1552 is represented by a title 1552A that identifies that therisk object is associated with an anomalous browser used by a user,along with a first risk score 1552B showing a risk score equal to 35.Those skilled in the art will understand that the titles 1551A and 1552Amay include any kind of descriptions of the risk objects, and riskscores 1551B and 1552B may include any type of risk scores or othercharacteristics associated with the risk objects, including, withoutlimitation, a risk score, a risk severity level, a risk probabilitylevel, an order or precedence indicator, and so forth.

Further, as shown, a title 1565 for the group of selected risk objects1561 may be displayed within the user selection box 1560. Specifically,as shown, the group of selected risk objects 1561 is identified as beingassociated with “Anomalous Login Source/Device,” indicating that theselected risk objects are associated with anomalous login activity.Those skilled in the art will understand that the title 1565 of thegroup of selected risk objects 1561 is displayed as a particulardescription, and that the title 1565 may include any kind ofdescriptions of the group of selected risk objects.

In addition, as shown, a representation of a logical operator portion1570 that identifies one or more logical operators for operating on orcombining the two selected risk objects 1551 and 1552 as a group may bedisplayed within the user selection box 1560. As shown, the logicaloperator portion 1570 specifies that the two selected risk objects 1551and 1552 are to be combined by determining a combined risk score bycombining the risk score 1551B with the risk score 1552B. Accordingly,the “result” generated by operating on or combining the two selectedrisk objects 1551 and 1552 is the combined risk score. Further, asshown, a “threat” is detected when a particular triggering condition ismet, namely, when the combined risk score is equal to 38. When thistriggering condition is met, then a corresponding “threat” is detected.Those skilled in the art will understand that the logical operatorportion 1570 is displayed as applying particular logical operators byway of example only, and that the logical operator portion 1570 may beimplemented via any one or more types of logical operators for operatingor combining risk objects and/or groups of risk objects, as describedherein.

As shown in FIG. 15C through 15E, additional groups of risk objects maybe selected and grouped together for further processing. As describedherein, a first group of selected risk objects 1561 is displayed, asdescribed herein, within the user selection box 1560. Also as shown, asecond group of selected risk objects 1581 is displayed within a userselection box 1580. Within this second group of selected risk objects1581 shown in user selection box 1580, a first sub-group of selectedrisk objects 1590 is displayed within a user selection box 1591, and asecond sub-group of selected risk objects 1595 are displayed within auser selection box 1596.

As shown, the first sub-group of selected risk objects 1590 is displayedwith a title 1592. Specifically, as shown, the title 1592 describes thefirst sub-group of selected risk objects 1590 as being associated with“User Profile Edit,” indicating that the selected risk objects areassociated with anomalous editing of user profiles. As further shown,the second sub-group of selected risk objects 1595 is displayed with atitle 1597. Specifically, as shown, the title 1597 describes the secondsub-group of selected risk objects 1595 as being associated with “MoneyMovement,” indicating that the selected risk objects are associated withanomalous financial transactions that involve moving money betweenaccounts. Those skilled in the art will understand that the titles 1592and 1597 are displayed as particular descriptions, and that the titles1592 and 1597 may include any kind of descriptions of the sub-groups ofrisk objects.

Further, as shown, each risk object included in the first sub-group ofselected risk objects 1590 is displayed with a title 1590A thatdescribes each corresponding risk object, and a risk score 1590B thatidentifies a risk score that is associated with each corresponding riskobject. Additionally, each risk object included in the second sub-groupof selected risk objects 1595 may be displayed with a title 1595A thatdescribes each corresponding risk object, and a risk score 1590B thatidentifies a risk score that is associated with each corresponding riskobject. Those skilled in the art will understand that the titles 1590Aand 1595A are displayed as particular titles by way of example only andmay include any kind of descriptions of the risk objects. Further, thoseskilled in the art will understand that the risk scores 1590B and 1595Bare displayed as particular risk scores by way of example only and mayinclude any types of risk scores or other characteristics associatedwith the risk objects, including, without limitation, a risk score, arisk severity level, a risk probability level, an order or precedenceindicator, and so forth.

The first sub-group of selected risk objects 1590 is also displayed witha logical operator portion 1593 that identifies one or more logicaloperators for operating on and/or combining the risk objects included inthe first sub-group of selected risk objects 1590. As shown, the logicaloperator portion 1593 specifies that the risk objects included in thefirst sub-group of selected risk objects 1590 are to be operated onand/or combined by determining a combined risk score and by determininga number of risk objects that are evaluated true, such as by determiningthe number of risk objects that produce any relevant machine data.Accordingly, the “results” generated by operating on and/or combiningthe risk objects included in the first sub-group of selected riskobjects 1590 include the combined risk score and the determined numberof risk objects that are true. Further, as shown, a “threat” is detectedwhen a particular triggering condition is met, namely, when the combinedscore is greater than 25, or when 2 or more of the risk objects areevaluated as true. When either one of these conditions is met, then acorresponding “threat” is detected.

Further, as shown, the second sub-group of selected risk objects 1595 isdisplayed with a logical operator portion 1598 that identifies one ormore logical operators for operating on and/or combining the riskobjects included in the second sub-group of selected risk objects 1595.As shown, the logical operator portion 1598 specifies that the riskobjects included in the second sub-group of selected risk objects 1595are to be operated on and/or combined by determining a combined riskscore. Accordingly, the “result” generated by operating on and/orcombining the risk objects included in the second sub-group of selectedrisk objects 1595 is the combined risk score. Further, as shown, a“threat” is detected when a particular triggering condition is met,namely, when the combined score is greater than 21. When this conditionsis met, then a corresponding “threat” is detected. Those skilled in theart will understand that the logical operator portions 1593 and 1598 aredisplayed as specifying particular logical operators by way of exampleonly and may be implemented via any one or more types of logicaloperators for operating and/or combining risk objects, as describedherein.

Further, as shown, an arrow 1575 may be displayed to represent a furtherlogical operator to connect between different groups of selected riskobjects, such as to connect the first group of selected risk objects1561 with the second group of selected risk objects 1581. In someembodiments, an arrow may represent a logical operator such as anif-then causation operator, such as, if a first group of selected riskobjects is triggered, then evaluate a next group of selected riskobjects to determine if that next group is also triggered. In someembodiments, an arrow may represent a logical operator such as an orderof precedence or time-order operator, such as, to determine if the nextgroup of selected risk objects is triggered after the first group ofselected risk objects is triggered. In some embodiments, an arrow mayrepresent a logical operator such as an OR operator or AND operator,where a combined trigger is met if either of the groups of selected riskobjects is triggered (i.e., an OR operator), or a combined trigger ismet only if both of the groups of selected risk objects are triggered(i.e., an AND operator). Further, an arrow may represent any other typeof logical operator to operate between groups of selected risk objects.

In addition, as shown in FIG. 15D, the arrow 1575 is completed,connecting the first group of selected risk objects 1561 displayed inthe user selection box 1560 with the second group of selected riskobjects 1581 displayed in the user selection box 1580. As describedherein, the completed arrow may represent any form of logical operatorto operate on or combine the first group of selected risk objects 1561and the second group of selected risk objects 1581, as described herein.

In addition, as shown in FIG. 15E, the second group of selected riskobjects 1581 is displayed with a title 1582. Specifically, as shown, thetitle 1582 describes the second group of selected risk objects 1581 asbeing associated with “Risky and Anomalous Session Activity.” Thoseskilled in the art will understand that the title 1582 is displayed as aparticular title by way of example only and may include any kind ofdescriptions of the second group of selected risk objects 1581.

As further shown, the second group of selected risk objects 1581 isdisplayed with a logical operator portion 1583 displayingrepresentations of logical operators to be applied to the second groupof selected risk objects 1581. The logical operator portion 1583includes selectable logical operators and selectable fields forselecting and determining the logical operators to apply to the secondgroup of selected risk objects 1581.

As shown, the logical operator portion 1583 is displayed as including aselectable logical operator field 1583A, which is represented asoperating on a combined risk score field of the selected risk objects.Those skilled in the art will understand that the selectable logicaloperator field 1583A is displayed as operating on a combined risk scoreby way of example only and may operate on any characteristic associatedwith the second group of selected risk objects 1581. As shown, thelogical operator portion 1583 is displayed as including selectablelogical operator fields 1583B and 1583E, which are represented asdrop-down or pull-down menus, and which represent the selection of the“greater than or equal to” operator. The logical operator portion 1583is also displayed as including selectable logical operator fields 1583Cand 1583F, which are represented as text input fields, and whichrepresent the selection of the text “250” and “3,” respectively. Thoseskilled in the art will understand that the selectable logical operatorfields 1583B, 1583C, 1583E, and 1583F may be selectable and utilize anykind of operators or text input. As shown, the logical operator portion1583 also is displayed as including a selectable logical operator field1583D, which is represented as an OR operation. The selectable logicaloperator field 1583D may be selectable, and selectable logical operatorfield 1583D is displayed as an OR operation by way of example only andmay utilize any type of logical operator.

As shown, the logical operator portion 1583 specifies that the riskobjects included in the second group of selected risk objects 1581 areto be operated on and/or combined by determining a combined risk scoreand by determining a number of risk objects that are evaluated true,such as by determining the number of risk objects that produce anyrelevant machine data. Accordingly, the “results” generated by operatingon and/or combining the risk objects included in the second group ofselected risk objects 1581 include the combined risk score and thedetermined number of risk objects that are true. Further, as shown, a“threat” is detected when a particular triggering condition is met,namely, when the combined score is greater than 250, or when 3 or moreof the risk objects are evaluated as true. When either one of theseconditions is met, then a corresponding “threat” is detected

In various embodiments, the selectable logical operator fields 1583Athrough 1583F may be represented and selected via the user interface andmay use any type of selection method. In some embodiments, a user mayselect and manipulate visual or graphical representations of the logicaloperators being displayed, select one or more logical operators via apull-down or drop-down menu, and/or input text representing the logicaloperators into a portion of the UI.

FIGS. 16A through 16B illustrate an example user interface for selectingrisk objects and applying logical operators to selected risk objects inaccordance with the disclosed embodiments. As shown, user interface 1600includes a risk object portion 1610 for displaying a listing of one ormore risk objects or groups of risk objects, where each risk object isassociated with a stored risk definition. In general, the risk objectportion 1610 may include any number of risk objects or groups of riskobjects related to any field or type of machine data, including, withoutlimitation, fraud, security, performance, business analytics, and soforth.

As shown, the user interface 1600 also includes a canvas portion 1620for displaying one or more selected risk objects or groups of riskobjects. Specifically, the canvas portion 1620 is shown as displaying afirst group of selected risk objects 1661. In some embodiments, arepresentation of a logical operator portion 1670 for operating on thefirst group of selected risk objects 1661 may be displayed. The logicaloperator portion 1670 may specify that the risk objects included in thefirst group of selected risk objects 1661 are to be combined bydetermining a combined risk score. Accordingly, the “result” generatedby operating on or combining the first group of selected risk objects1661 is the combined risk score. Further, as shown, a “threat” isdetected when a particular triggering condition is met, for example,when the combined risk score is equal to 38. When this triggeringcondition is met, then a corresponding “threat” is detected.

Canvas portion 1620 may display an actions portion 1671 that includesone or more actions that are to be performed when a “threat” isdetected. Specifically, when a triggering condition (e.g., a threat) ismet, a user may select certain actions that will be performed inresponse to the triggering condition. As shown in FIG. 16A, the actionsportion 1671 includes generating an alert 1672, running a script 1673,and/or sending an email 1674 indicating that the triggering conditionhas been met. For example, when the combined risk score for the firstgroup of selected risk objects 1661 is equal to 38, the triggeringcondition specified by logical operator portion 1670 is met, and, inresponse, a selected action may be performed. Those skilled in the artwill understand that the exemplary actions shown in actions portion 1671are displayed as particular actions by way of example only, and that theexemplary actions to be performed may be any one or more technicallyfeasible actions, as described herein.

As further shown in FIG. 16B, the canvas portion 1620 displays a previewsection 1676 for displaying a preview of “threats” that would bedetected if the logical operators specified in logical operator portion1670 were to be applied to the first group of selected risk objects1661. For example, the preview section 1676 is shown as including atitle 1677 indicating that the threat includes a possible accounttakeover. In addition, the preview section 1676 is shown as including aan severity level 1678 indicating a severity level of the threat, forinstance, that the severity level of the potential risk or issues is“critical.”

FIG. 17 illustrates an example dashboard screen for displaying status ofmonitoring machine data related to risk objects in accordance with thedisclosed embodiments. As shown, FIG. 17 shows a dashboard screen 1700being displayed that includes a risk object portion 1710 for displayinga listing of one or more risk objects and/or groups of risk objects,where each risk object is associated with a stored risk definition, asdescribed herein. As shown, the risk object portion 1710 displays atitle 1711 describing a risk object, and the risk object portion 1710displays a risk score 1712 representing a risk score associated with therisk object. For instance, the risk object portion 1710 displays therisk score 1712 in the form of a number and a color code, as indicatedby shading.

In addition, the dashboard screen 1700 being displayed includes asummary section 1720 for summarizing the relevant threats found fromsearching the machine data. As shown, the summary section 1720 includesa title 1721 describing the relevant threats and a threat level 1722 inthe form of a color code indicating a score, severity, or urgency levelof the relevant threat, as described herein. In addition, in someembodiments, the dashboard screen 1700 includes a chart/diagram portion1730 for displaying representations of different portions of relevantmachine data, such as transactions involving money movement, in ascatter diagram. Those skilled in the art will understand that thechart/diagram portion 1730 may include any technically feasible types ofcharts or diagrams to represent the machine data found to be relevant toone or more particular searches, and that the relevant characteristicsbeing sought are shown as transactions involving money movement by wayof example only.

In addition, in some embodiments, the dashboard screen 1700 beingdisplayed includes a timeline portion 1740 for displayingrepresentations of different portions of relevant data in the form of atimeline. In particular, relevant data may be graphically represented ona timeline in order to more clearly illustrate how the data follows anexpected sequence or deviates from that sequence. As shown, the timelineportion 1740 shows a timeline of normal activity and a divergenttimeline of suspicious activity, in order to more clearly delineate to aviewer or user the differences between the normal activity and thesuspicious activity and to illustrate when the suspicious activitybegan.

Further, in some embodiments, the dashboard screen 1700 being displayedincludes an actions portion 1750 for displaying actions to be performedwhen a rule or triggering condition is met and a threat is detected. Asshown, the actions portion 1750 displays exemplary actions that may beperformed, including suppressing a detected threat 1751, creating aticket 1752 to address a threat, or sharing a threat 1753 (e.g., byissuing an alert).

FIG. 18 is a flow diagram of method steps for creating and storing arisk definition, in accordance with the disclosed embodiments. Althoughthe method steps are described in conjunction with the systems of FIGS.1-2 and 9-12, those skilled in the art will understand that any systemconfigured to perform the method steps, in any order, is within thescope of the present invention.

As shown, a method 1800 begins at step 1810, where a risk monitoringsystem 1116 creates a risk definition to define the specific criteriaused to determine whether machine data (e.g., raw machine data) isrelevant to a particular risk or condition. In some embodiments, therisk definitions may include the one or more search query commandsthemselves, information specifying the applicable search query commands,the machine-learning algorithms themselves, information specifying theapplicable machine-learning algorithms, or any form of informationidentifying the criteria for determining whether machine data isrelevant to a particular risk or condition. In some embodiments, therisk definitions may be defined or updated by a user, may bepredetermined, or may be determined in an automated fashion, asdescribed herein.

As step 1820, the risk monitoring system 1116 causes the riskdefinitions to be stored in a memory for future use for future use inorder to enable a user to perform future searches of machine data basedon accessing the stored risk definitions. In this manner, the riskmonitoring system 1116 may perform future searches of machine data basedon accessing the stored risk definitions, without requiring the user torepeatedly enter complicated search query commands or machine-learningalgorithms each time a search is performed. In some embodiments, therisk definitions may be stored in the risk monitoring system 1116, inthe data intake and query system 108, in any of the host devices 106, orin any of the client devices 102 in any other manner.

At step 1830, the risk monitoring system 1116 may cause representationsof a risk object, which corresponds to a stored risk definition, to bedisplayed to a user via a user interface (UI). The risk object isrepresented to the user to identify a search of data based on the storedrisk definition and/or to identify a characteristic associated with thedata resulting from the search, such as a risk score resulting from thesearch, as further described herein. In some embodiments, the riskobject may be represented graphically via the UI. In some embodiments,the risk monitoring system 1116 may search the machine data based on arisk definition in response to a user action (e.g., a user request), ina predetermined or automated manner (e.g., on a periodic basis or at aspecified time), or in any other manner.

FIG. 19 is a flow diagram of method steps for generating a result basedon one or more risk objects and one or more logical operators that arespecified via a UI, in accordance with the disclosed embodiments.Although the method steps are described in conjunction with the systemsof FIGS. 1-2 and 9-12, those skilled in the art will understand that anysystem configured to perform the method steps, in any order, is withinthe scope of the present invention.

As shown, a method 1900 begins at step 1910, where the risk monitoringsystem 1116 searches the machine data for relevant data based on thecriteria specified in a stored risk definition, and causes arepresentation of a risk object to be displayed via the UI to identifythe search of the machine data based on the stored risk definitionand/or to identify a characteristic associated with the machine dataresulting from the search, such as a risk score resulting from thesearch. As described herein, the risk object may be represented by a UIelement that includes any type of risk score or other characteristicsassociated with the risk object, including, without limitation, a riskscore, a risk severity level, a risk probability level, an order ofprecedence indicator, and so forth

In step 1920, the risk monitoring system 1116 may receive a selection ofone or more risk objects via any of the selection mechanisms describedherein. The risk monitoring system 1116 receives a selection, including,without limitation, a selection by a user or an automated selection, ofone or more of the displayed risk objects and/or groups of risk objectsin order to generate some form of result based on the selected riskobjects and/or groups of risk objects. In some embodiments, the user mayselect the one or more risk objects and/or groups of risk objects viaany form of selection mechanism, such as by a user selection via the UI(e.g., by dragging-and-dropping the selected risk objects onto a canvasincluded in the UI and/or using highlighting or selection boxmechanisms), any form of keyboard interaction, and so forth.

In step 1930, the risk monitoring system 1116 causes a representation ofone or more logical operators for operating on or combining the selectedrisk objects and/or groups of risk objects to be displayed via the UI.In some embodiments, the logical operators may include any form ofoperators for operating on or combining information representing theselected risk objects in any technically-feasible manner. In variousembodiments, the representations of the logical operators displayed inthe UI may include one or more fields for a user to input or select thelogical operators to be used. For instance, in the selection of the oneor more logical operators, a user may input information for theoperators, such as via a text input field, or a user may selectoperators, such as a via a graphical pull-down or drop-down menu, and soforth.

In step 1940, the risk monitoring system 1116 receives a selection froma user of one or more logical operators being displayed via the UI, inorder to generate a result based on the one or more selected riskobjects and/or groups of risk objects and the one or more selectedlogical operators.

In step 1950, once the risk monitoring system 1116 receives a selectionof the one or more risk objects and/or groups of risk objects and theone or more logical operators, the risk monitoring system 1116 causes aresult to be generated by operating on and/or combining the selected oneor more risk objects and/or groups of risk objects using the selectedone or more logical operators. For example, as described herein, therisk monitoring system 1116 could perform one or more search queriesspecified by one or more risk definitions and process the results basedon one or more logical operators. The generated result may include someform of metric or indicator identifying a characteristic of the datafound to be relevant to the selected one or more risk objects and/orgroups of risk objects.

Further, the risk monitoring system 1116 determines whether a “threat”is detected based on the generated result of operating on and/orcombining the selected one or more risk objects and/or groups of riskobjects. In various embodiments, a user may select one or more logicaloperators, in a manner as described herein, to determine whether theresults generated by operating on and/or combining the selected riskobjects meets certain conditions.

In step 1960, the risk monitoring system 1116 performs an “action” whena threat is detected. In some embodiments, an action performed by therisk monitoring system 1116 may vary depending on the relevant field ortype of data associated with the search. For instance, when searchingfor and analyzing machine data related to fraud, security, performanceissues, or business analytics, an action performed by the riskmonitoring system 1116 may include, without limitation, issuing awarning regarding detected threats, sending an email to a particularuser's address when a threat is detected, generating a ticket to beprocessed to remedy the threat, running a computer script, mitigatingthe threat, or suppressing the threat when the threat is determined tobe a false alarm.

FIG. 20A is a flow diagram of method steps for applying various types oflogic to generate a result in a risk monitoring system, in accordancewith the disclosed embodiments. Although the method steps are describedin conjunction with the systems of FIGS. 1-2 and 9-12, those of skill inthe art will understand that any system configured to perform the methodsteps, in any order, is within the scope of the present invention.

In some embodiments, one or more of the steps of method 2000 may beperformed at step 1950 in the method 1900 of FIG. 19. As shown, themethod 2000 begins at optional step 2010, where the risk monitoringsystem 1116 applies the one more selected logical operators to operateon or combine the risk objects based on the data determined to berelevant to one or more searches associated with the selected riskobjects and/or groups of risk objects. For instance, the risk monitoringsystem 1116 may determine whether a search produced any relevant machinedata, in order to determine whether a risk object can be evaluated as“true,” as described herein. Further, the risk monitoring system 1116may determine whether a “threat” is detected based on the generatedresults, such as that a threat is detected when at least a certainnumber of risk objects are evaluated as “true.”

At optional step 2020, the risk monitoring system 1116 applies the onemore selected logical operators to operate on or combine the riskobjects based on the risk scores associated with the selected riskobjects and/or groups of risk objects. For instance, the risk monitoringsystem 1116 may determine a combined risk score associated with theselected risk objects. Further, the risk monitoring system 1116 maydetermine whether a “threat” is detected based on the generated results,such as that a threat is detected when a combined risk score meets aparticular threshold value.

At optional step 2030, the risk monitoring system 1116 applies the onemore selected logical operators to operate on or combine the riskobjects based on the risk severity levels associated with the selectedrisk objects and/or groups of risk objects. For instance, the riskmonitoring system 1116 may determine a combined or threshold riskseverity level associated with the risk objects and/or groups of riskobjects. Further, the risk monitoring system 1116 may determine whethera “threat” is detected based on the generated results, such as that athreat is detected when a combined risk severity level meets aparticular threshold value.

At optional step 2040, the risk monitoring system 1116 applies the onemore selected logical operators to operate on or combine the riskobjects based on the order of precedence or time order of the riskobjects. For instance, the risk monitoring system 1116 may determine acertain order of precedence or time order based on the selected riskobjects and/or groups of risk objects. Further, the risk monitoringsystem 1116 may determine whether a “threat” is detected based on thegenerated results, such as that a threat is detected when a particularrisk object is associated with earlier activity, followed by anotherrisk object associated with later activity, such as when the machinedata indicates that certain actions occur followed by certain specifiedtransactions, such as when an account password is changed, followed by atransfer of money out of the account.

At optional step 2050, the risk monitoring system 1116 applies the onemore selected logical operators to operate on or combine the riskobjects based on the risk objects and/or groups of risk objects meetingcertain specified conditions. Further, the risk monitoring system 1116may determine whether a “threat” is detected based on the generatedresults, such as that a threat is detected when the selected riskobjects and/or machine data associated with the selected risk objectsmeet any other defined conditions.

At optional step 2060, the risk monitoring system 1116 applies the onemore selected logical operators to operate on or combine the riskobjects based on any other form of logical operator associated with theselected risk objects and/or groups of risk objects, or any combinationof the above logical operators.

FIG. 20B is a flow diagram of method steps for applying various types oflogic to generate a result in a risk monitoring system, in accordancewith the disclosed embodiments. Although the method steps are describedin conjunction with the systems of FIGS. 1-2 and 9-12, those of skill inthe art will understand that any system configured to perform the methodsteps, in any order, is within the scope of the present invention.

In some embodiments, one or more of the steps of method 2000 may beperformed at step 2050 in the method 2000 of FIG. 20A. As shown, themethod 2001 begins at optional step 2051, where the risk monitoringsystem 1116 applies one or more AND-based logical operators to operateon or combine the selected one or more risk objects and/or groups ofrisk objects based on the risk objects and/or the machine dataassociated with the risk objects meeting certain specified conditions.For instance, the risk monitoring system 1116 may determine that, inorder for a triggering condition to be met and thus for a “threat” to bedetected, certain conditions must be met by the risk objects, and otherconditions must be met as well.

At optional step 2052, the risk monitoring system 1116 applies one ormore OR-based logical operators to operate on or combine the selectedone or more risk objects and/or groups of risk objects based on the riskobjects and/or the machine data associated with the risk objects meetingcertain specified conditions. For instance, the risk monitoring system1116 may determine that, in order for a triggering condition to be metand thus for a “threat” to be detected, either certain conditions mustbe met, or other conditions must be met.

At optional step 2053, the risk monitoring system 1116 applies one ormore count-based logical operators to operate on or combine the riskobjects and/or groups of risk objects based on the how many of the riskobjects meet certain specified conditions. For instance, the riskmonitoring system 1116 may determine that, in order for a triggeringcondition to be met and thus for a “threat” to be detected, a certainnumber of risk objects must meet certain conditions, and/or a riskobject must meet a certain number of conditions. For instance, oneexemplary triggering condition could require that three out of four riskobjects must be evaluated as “true,” or a risk score for three out offour risk objects must exceed a particular threshold..

At optional step 2054, the risk monitoring system 1116 applies any othertype of logical operators, or any combination of the above logicaloperators, to operate on or combine the risk objects and/or groups ofrisk objects based on the risk objects and/or the machine dataassociated with the risk objects that meet certain specified conditions.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. In one example,and without limitation, the techniques described herein are implementedin conjunction with the system architecture described in conjunction inFIGS. 1-20B. However, the described techniques could be implemented inconjunction with any technically feasible system architecture thatgenerates the requisite data upon which the disclosed techniques arebased. In particular, the risk event monitoring system 1116 could beimplemented to receive and analyze raw machine data and/or any otherform of data in any technically feasible format. Further, the risk eventmonitoring system 1116 could analyze data received from the data intakeand query system 108 described in conjunction with FIGS. 1-10, from anyalternative computer system capable of generating such data, or anytechnically feasible combination thereof.

In sum, a risk event monitoring system causes a representation of theone or more risk objects to be displayed via a user interface (UI). Therisk monitoring system receives a selection of one or more risk objectsand/or groups of risk objects displayed in the UI and causes arepresentation of one or more logical operators to be displayed via theUI. The risk monitoring system then receives a selection of one or moreof the logical operators displayed in the UI. Further, the riskmonitoring system causes a result to be generated based on the selectedrisk objects and based on the selected logical operator(s). Finally, therisk monitoring system determines whether a threat is detected based onthe generated results and optionally performs one or more actions inresponse to detecting the threat.

At least one advantage of the disclosed techniques is that, by storingthe risk definitions and searching and analyzing the machine data basedon the stored risk definitions, the risk monitoring system enablesmachine data to be searched without requiring a user to re-entercomplicated search query commands and/or machine-learning algorithmseach time a search is performed. A further advantage of the disclosedtechniques is that, by representing the risk objects and logicaloperators graphically, a user without specific proficiency or technicalknowledge related to a computer language syntax is able to perform andmanipulate searches efficiently.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, methodor computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, enable the implementation of the functions/acts specified inthe flowchart and/or block diagram block or blocks. Such processors maybe, without limitation, general purpose processors, special-purposeprocessors, application-specific processors, or field-programmable gatearrays.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While the preceding is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A computer-implemented method, comprising:providing a representation of a plurality of risk objects and one ormore logical operators, wherein a first risk object included in theplurality of risk objects is associated with first raw machine datareflecting activity in an information technology (IT) environmentpertaining to the first risk object, and wherein a second risk objectincluded in the plurality of risk objects is associated with second rawmachine data reflecting activity in the IT environment pertaining to thesecond risk object; receiving a selection of a first logical operatorincluded in the one or more logical operators, wherein the first logicaloperator defines a relationship between the first risk object and thesecond risk object; and performing, based on the first logical operator,a search of the first raw machine data and the second raw machine data.2. The method of claim 1, wherein a risk is identified based on thesearch of the raw machine data.
 3. The method of claim 1, wherein afirst risk definition corresponding to the first risk object associatesthe first risk object with the first raw machine data, wherein a secondrisk definition corresponding to the second risk object associates thesecond risk object with the second raw machine data, and whereinperforming the search comprises accessing, based on the first riskdefinition and the second risk definition, the first raw machine dataand the second raw machine data.
 4. The computer-implemented method ofclaim 1, wherein performing the search comprises evaluating the firstraw machine data and the second raw machine data to true or false basedon the first logical operator.
 5. The computer-implemented method ofclaim 1, wherein the first logical operator causes a risk to beidentified when at least one of the first risk object and a second riskobject included in the one or more risk objects evaluates as true. 6.The computer-implemented method of claim 1, wherein the first rawmachine data is included in one or more events, each event beingassociated with a different timestamp and including raw machine datareflective of activity in the IT environment pertaining to the firstrisk object.
 7. The computer-implemented method of claim 1, wherein thefirst risk object is associated with a first risk score, the second riskobject is associated with a second risk score, a result is generated bycombining, based on the first logical operator, the first risk score andthe second risk score, and the search is performed based on the result.8. The computer-implemented method of claim 1, wherein the first riskobject is associated with a first severity level, the second risk objectis associated with a second severity level, a result is generated bycombining, based on the first logical operator, the first severity leveland the second severity level, and the search is performed based on theresult.
 9. The computer-implemented method of claim 1, wherein therepresentation of the plurality of risk objects and the one or morelogical operators is provided in a graphical user interface.
 10. Thecomputer-implemented method of claim 1, wherein the representation ofthe plurality of risk objects and the one or more logical operators isprovided in a graphical user interface, and the selection of the firstlogical operator is received via the graphical user interface.
 11. Oneor more non-transitory computer readable storage media storinginstructions that, when executed by one or more processors, cause theone or more processors to perform the steps of: providing arepresentation of a plurality of risk objects and one or more logicaloperators, wherein a first risk object included in the plurality of riskobjects is associated with first raw machine data reflecting activity inan information technology (IT) environment pertaining to the first riskobject, and wherein a second risk object included in the plurality ofrisk objects is associated with second raw machine data reflectingactivity in the IT environment pertaining to the second risk object;receiving a selection of a first logical operator included in the one ormore logical operators, wherein the first logical operator defines arelationship between the first risk object and the second risk object;and performing, based on the first logical operator, a search of thefirst raw machine data and the second raw machine data.
 12. The one ormore non-transitory computer readable storage media of claim 11, whereina risk is identified based on the search of the raw machine data. 13.The one or more non-transitory computer readable storage media of claim11, wherein a first risk definition corresponding to the first riskobject associates the first risk object with the first raw machine data,wherein a second risk definition corresponding to the second risk objectassociates the second risk object with the second raw machine data, andwherein performing the search comprises accessing, based on the firstrisk definition and the second risk definition, the first raw machinedata and the second raw machine data.
 14. The one or more non-transitorycomputer readable storage media of claim 11, wherein performing thesearch comprises evaluating the first raw machine data and the secondraw machine data to true or false based on the first logical operator.15. The one or more non-transitory computer readable storage media ofclaim 11, wherein the first logical operator causes a risk to beidentified when at least one of the first risk object and a second riskobject included in the one or more risk objects evaluates as true. 16.The one or more non-transitory computer readable storage media of claim11, wherein the first raw machine data is included in one or moreevents, each event being associated with a different timestamp andincluding raw machine data reflective of activity in the IT environmentpertaining to the first risk object.
 17. The one or more non-transitorycomputer readable storage media of claim 11, wherein the first riskobject is associated with a first risk score, the second risk object isassociated with a second risk score, a result is generated by combining,based on the first logical operator, the first risk score and the secondrisk score, and the search is performed based on the result.
 18. The oneor more non-transitory computer readable storage media of claim 11,wherein the first risk object is associated with a first severity level,the second risk object is associated with a second severity level, aresult is generated by combining, based on the first logical operator,the first severity level and the second severity level, and the searchis performed based on the result.
 19. The one or more non-transitorycomputer readable storage media of claim 11, wherein the representationof the plurality of risk objects and the one or more logical operatorsis provided in a graphical user interface.
 20. The one or morenon-transitory computer readable storage media of claim 11, wherein therepresentation of the plurality of risk objects and the one or morelogical operators is provided in a graphical user interface, and theselection of the first logical operator is received via the graphicaluser interface.
 21. A computer system, comprising: one or more memorysub-systems storing instructions; and one or more processors forexecuting the instructions to: provide a representation of a pluralityof risk objects and one or more logical operators, wherein a first riskobject included in the plurality of risk objects is associated withfirst raw machine data reflecting activity in an information technology(IT) environment pertaining to the first risk object, and wherein asecond risk object included in the plurality of risk objects isassociated with second raw machine data reflecting activity in the ITenvironment pertaining to the second risk object, receive a selection ofa first logical operator included in the one or more logical operators,wherein the first logical operator defines a relationship between thefirst risk object and the second risk object, and perform, based on thefirst logical operator, a search of the first raw machine data and thesecond raw machine data.
 22. The computer system of claim 21, wherein arisk is identified based on the search of the raw machine data.
 23. Thecomputer system of claim 21, wherein a first risk definitioncorresponding to the first risk object associates the first risk objectwith the first raw machine data, wherein a second risk definitioncorresponding to the second risk object associates the second riskobject with the second raw machine data, and wherein performing thesearch comprises accessing, based on the first risk definition and thesecond risk definition, the first raw machine data and the second rawmachine data.
 24. The computer system of claim 21, wherein performingthe search comprises evaluating the first raw machine data and thesecond raw machine data to true or false based on the first logicaloperator.
 25. The computer system of claim 21, wherein the first logicaloperator causes a risk to be identified when at least one of the firstrisk object and a second risk object included in the one or more riskobjects evaluates as true.
 26. The computer system of claim 21, whereinthe first raw machine data is included in one or more events, each eventbeing associated with a different timestamp and including raw machinedata reflective of activity in the IT environment pertaining to thefirst risk object.
 27. The computer system of claim 21, wherein thefirst risk object is associated with a first risk score, the second riskobject is associated with a second risk score, a result is generated bycombining, based on the first logical operator, the first risk score andthe second risk score, and the search is performed based on the result.28. The computer system of claim 21, wherein the first risk object isassociated with a first severity level, the second risk object isassociated with a second severity level, a result is generated bycombining, based on the first logical operator, the first severity leveland the second severity level, and the search is performed based on theresult.
 29. The computer system of claim 21, wherein the representationof the plurality of risk objects and the one or more logical operatorsis provided in a graphical user interface.
 30. The computer system ofclaim 21, wherein the representation of the plurality of risk objectsand the one or more logical operators is provided in a graphical userinterface, and the selection of the first logical operator is receivedvia the graphical user interface.